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4,222 Introduction to Programming

Lecture 1: Introduction

Dr. Franziska Bender, Dr. Aurélien Sallin

2026-02-20

Welcome to Introduction to Programming!

Vision for this course

It’s an introductory course, and it is not

Introductory yes, but not in the traditional sense of the term.

You know R, Data Handling, some of you did DSF, you are learning Econometrics.

By the end of the course, you will be able to…

  • Use essential tools for working with data
    We will work with the programming language R, but the concepts apply to any programming language.
  • Manage a data project from start to finish
    You will practice collecting, cleaning, and analyzing data to carry out a complete project in Economics (research, consulting, …).
  • Formulate meaningful questions from data
    You will learn how to explore datasets and identify relevant questions.
  • Communicate insights effectively
    You will gain experience in presenting results clearly and persuasively.

Our commitment to these goals and to your learning process

  • Transferrable skills
  • Hands-on approach
  • Emphasis on real-world relevance
    (caveat: this course is mandatory for Econ students, I have limited freedom in the syllabus)
  • As much fun as possible (as coding can be fun…😎)

Your commitment to the course

  • Prepare with reading, visit the lecture, recap key concepts in lecture notes (self-study)
  • Work on exercises, come to exercise session, tackle the tricky exercises together!
  • Code, code, and code. repeat…
  • 🤖 Use AI as a learning companion and sparring partner, not as a crutch 🩼! (more on that later)

About us

Organisation of the Course

Course concept: lectures

  • Lectures (Thursday morning)
    • Background/Concepts
    • Illustration of concepts
    • Illustration of ‘hands-on’ approaches

Course concept: exercises

  • Exercise sheets (handed out every other week)
    • Some conceptual questions
    • Hands-on exercises/tutorials in R
    • First Exercises (set up R/RStudio) is available on StudyNet/Canvas today

Course concept: exercise sessions

  • In-class exercise sessions (bi-weekly evening sessions)
    • Discussion of exercises and additional input with Federica and Andrea
    • Recap of concepts
    • Q&A, support
    • time for more coding!

Course concept: a note

This is the first time we give this course.

The materials are new, the syllabus is new.

The road ahead

Part I: Data (Science) fundamentals

Exam information

The examination consists of two parts:

  • Group written project, 35% of final grade.
  • Short examination on the group project, 15% of final grade.
  • Central written examination, 50% of final grade.

Exam information: group project

Goals

  • Use real-world data in a project to practice and reinforce the concepts covered in the course.

Project timeline

  • October 16: Release of Part 1 of the project (in lecture) and group formation.

  • November 6, 23:59: Intermediate submission (not graded).

  • November 7: Release of model solution for Part 1 and release of Part 2 of the project.

  • December 11, 10:00: Final submission (graded).

The entire project will be evaluated after the final submission.

Exam information: short examination

Goal

Beyond the critical use of LLMs, students must demonstrate their own grasp of the material and ensure fair collaboration in group work, avoiding free-riding.


Important information

  • Date: December 11th at the beginning of the lecture.
  • Written examination: digital, BYOD!.
  • Duration: 20mn
  • This short exam consists of 2–3 questions about the homework project.

The tools

Core course resources

  • All information and materials (notes, slides, course sheet, syllabus, etc.) are available on Canvas.

  • Use github to be always updated about the course material

    • Install git on your computer as explained here
    • Clone the course repository using:
git clone https://github.com/ASallin/datahandling-lecture.git # to clone
git checkout 2025 # to switch to the branch "2025*
git pull origin 2025 # to update

How to best use AI for this course


1. Use AI as your Learning Companion

❌ Ask AI to write full programs for you

✅ Ask structured questions based on your own knowledge (idea: knowledge reinforcement)

✅ Use AI to explore unknown and new approaches

You want to compute the mean of each element of the list x.

x <- list(
  c(1,2,3),
  c(4,5,6)
  )
  • Ask AI: “I know that the function to compute the mean is mean(), and I know that it takes atomic vectors as argument. How do I apply the mean to each vector in the list? Give me two options, and explain pros/cons?”

2. Use AI to better understand your code

✅ Use AI as a translator

✅ Ask for explanations in plain language

✅ Get step-by-step descriptions

You have the following piece of code:

mat <- matrix(c(1,2,3, 11,12,13), nrow = 2, ncol = 3,
             byrow = TRUE,
             dimnames = list(c("row1", "row2"),
                             c("C.1", "C.2", "C.3")))
mat[1,] <- mat[1, ] + 10
mat[1,] == mat[2,]
 C.1  C.2  C.3 
TRUE TRUE TRUE

Ask AI: “Explain line by line what this R code does in plain English. What does the option byrow = TRUE do?”

3. Use AI to debug

✅ Share error messages and context

✅ Ask for possible causes

✅ Ask for alternative solutions

You try:

log("text")

R sends the following error message: “non-numeric argument to mathematical function”

Ask AI: “Why does this error occur in R and how can I fix it?”

4. Use AI as your Motivational Coach 🔥

✅ Ask for tips to stay motivated and understand WHY you’re learning what you’re learning

✅ Ask for ways to make coding more enjoyable and directly relevant to your goals

✅ Ask for ways to make your code and thinking process more like the product of experts

You don’t see the point of learning to code functions and loops since AI can do it for you.

Ask AI: “Why is learning to program functions important for an economist/data scientist? Give me some real-life examples.”

Questions?